Large Scale Visualization on the Cray XT3 Using ParaView
|
|
- Leslie Townsend
- 5 years ago
- Views:
Transcription
1 Large Scale Visualization on the Cray XT3 Using ParaView Cray User s Group 2008 May 8, 2008 Kenneth Moreland David Rogers John Greenfield Sandia National Laboratories Alexander Neundorf Technical University of Kaiserslautern Berk Geveci Patrick Marion Kitware, Inc. Kent Eschenberg Pittsburgh Supercomputing Center Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy s National Nuclear Security Administration under contract DE-AC04-94AL85000.
2 Motivation We ve spent 20 years developing specialized hardware for graphics/visualization applications. Originally, it was customary to move data from the simulation platform to the visualization platform. Simulation Platform Move Data Visualization Platform
3 Motivation Moving data from large simulations is prohibitive. It can take from hours to weeks depending on the data and the connection. Simulation Platform Move Data Visualization Platform
4 Motivation Moving data from large simulations is prohibitive. It can take from hours to weeks depending on the data and the connection. We ve learned to co-locate the visualization platform with the data. Or at least have a dedicated high speed network. Simulation Platform Data Storage Visualization Platform
5 Motivation Bottlenecks are (in order): File data management (moving/reading). Data Processing (isosurfaces, mathematics, computational geometry, etc.). Rendering. In fact, we ve had great success deploying on clusters with no graphics hardware. Simulation Platform Data Storage Visualization Platform
6 Ideal Visualization Computer Cloud 9: A large parallel computer with direct access to data files and dedicated graphics hardware.
7 Ideal Visualization Computer Cloud 9: A large parallel computer with direct access to data files and dedicated graphics hardware. Cloud 8.5: A large parallel computer with direct access to data files. Hey, that could be the Cray thingy we ran the simulation on.
8 Problem with Interactive Visualization For real time interaction, you need a remote connection to a GUI (usually through a socket). Client Socket 0 Server GUI Scripting
9 Problem with Interactive Visualization For real time interaction, you need a remote connection to a GUI (usually through a socket). No sockets with Catamount Client Socket 0 Server GUI Scripting
10 Problem with Interactive Visualization For real time interaction, you need a remote connection to a GUI (usually through a socket). We get around this problem by removing the client altogether and move the scripting over to the parallel server, which is doing the heavy lifting. Scripting 0 Scripted Server
11 Ideal Visualization Computer Cloud 9: A large parallel computer with direct access to data files and dedicated graphics hardware and on-demand interactive visualization. Cloud 8.5: A large parallel computer with direct access to data files and on-demand interactive visualization. Cloud 6.2: A large parallel computer with direct access to data files and scriptable visualization.
12 Why Not Portals? Previous work has solved the same problem using VisIt and portals. Even if we implemented this, we may not be able to use it. Use of portals this way concerns administrators. Extra network traffic. Security issues (limit comm in/out compute nodes). Allocated nodes sitting idle waiting for user input (common during interactive) is frowned upon. Compute time is expensive. Job queues cannot quickly start interactive jobs. Compute time is expensive, nodes constantly busy. We could pursue this, but are unmotivated.
13 Implementation Details Python for Catamount. OpenGL for Catamount with no rendering hardware. Cross Compiling ParaView source.
14 Python for Catamount Initial port completed last year. Reported implementation at CUG No dynamic libraries: compile modules statically. Must know modules a-priori. Cannot directly execute cross-compiled executables. Used yod to get return codes for the configure/build process. Other Catamount-specific build configuration.
15 Python for Catamount Improvement for 2008: CMake build scripts Created CMake build scripts to use in place of Python s autoconf scripts. Leverages cross-compile support implemented for ParaView source (discussed later). Toolchain files (small system-specific scripts) set up cross-compile build parameters. Set up Catamount-specific configuration. Can pass configure/build runtime checks. Don t need to use yod during build. Makes specifying modules to statically link easier to select.
16 OpenGL for Catamount Use Mesa 3D: the de facto software implementation of OpenGL. Also contains code for using OpenGL without an X11 host. Mesa 3D build comes with cross-compile support. We added cross-compile configuration for Catamount. Our configuration is now included with Mesa 3D version and later.
17 Cross Compiling ParaView ParaView uses the CMake build tool. 12 months ago CMake had no special support for cross-compilation, and it was difficult. Added explicit cross-compilation controls. Toolchain files make specifying target system parameters simple. Try run queries are skipped. CMake variables simplify hand-coding the info. CMake creates an editable script that can be edited to the target system s behavior to automate filling these variables. A completed script is packaged with the ParaView source code.
18 Cross Compiling ParaView ParaView build creates programs that generate new source code to compile. Example: ParaView builds a program that parses VTK header files and generates C++ code for Python bindings. These programs cannot be run locally when crosscompiling. Solution: build ParaView twice, once for the local machine and once for the target machine. Target machine uses programs from the local build. CMake options to build only these intermediate applications trivializes the local build time.
19 Example Usage ParaView deployed on Bigben Cray XT3 at Pittsburgh Supercomputing Center. Bigben used by institutions around the country. Many users have no direct access to visualization resources local to PSC. Moving simulation results time consuming. Instead, perform visualization on Bigben. Visualization results typically much smaller than simulation results.
20 Hydrodynamic Turbulence and Star Formation in Molecular Clouds Alexei Kritsuk, University of California, San Diego. Density on grid.
21 Scaling Time to Render and Save a Frame with Respect to Job Size 1000 Time per Frame (sec) Measured Values Ideal Scaling Processes
22 Conclusions The port of ParaView to Cray XT3 ready for anyone wishing to use it. Scripted visualization is straightforward and scalable. Interactive visualization on the Cray XT3 is unavailable due to the lack of sockets. Anyone interested could implement communication in and out of compute nodes if they have a use case (any takers?). As Cray is moving to Compute Node Linux, which supports sockets, the point is probably moot.
Large Scale Visualization on the Cray XT3 Using ParaView
Large Scale Visualization on the Cray XT3 Using ParaView Kenneth Moreland, Sandia National Laboratories David Rogers, Sandia National Laboratories John Greenfield, Sandia National Laboratories Berk Geveci,
More informationEnabling Contiguous Node Scheduling on the Cray XT3
Enabling Contiguous Node Scheduling on the Cray XT3 Chad Vizino vizino@psc.edu Pittsburgh Supercomputing Center ABSTRACT: The Pittsburgh Supercomputing Center has enabled contiguous node scheduling to
More informationResponsive Large Data Analysis and Visualization with the ParaView Ecosystem. Patrick O Leary, Kitware Inc
Responsive Large Data Analysis and Visualization with the ParaView Ecosystem Patrick O Leary, Kitware Inc Hybrid Computing Attribute Titan Summit - 2018 Compute Nodes 18,688 ~3,400 Processor (1) 16-core
More informationApplication Sensitivity to Link and Injection Bandwidth on a Cray XT4 System
Application Sensitivity to Link and Injection Bandwidth on a Cray XT4 System Cray User Group Conference Helsinki, Finland May 8, 28 Kevin Pedretti, Brian Barrett, Scott Hemmert, and Courtenay Vaughan Sandia
More informationHigh-Performance Remote File I/O for the XT3 Nathan Stone Pittsburgh Supercomputing Center a.k.a. PITTSC
High-Performance Remote File I/O for the XT3 Nathan Stone Pittsburgh Supercomputing Center a.k.a. PITTSC Nathan Stone - Portals Direct I/O - CUG'06 Explicit User Demand (!) The Woodward collaboration (umn.edu)
More informationUsing the Emulab network testbed to evaluate the Armada I/O framework for computational grids
Using the Emulab network testbed to evaluate the Armada I/O framework for computational grids Ron Oldfield and David Kotz Dartmouth Technical Report TR2002-433 Department of Computer Science Dartmouth
More informationHashing Strategies for the Cray XMT MTAAP 2010
Hashing Strategies for the Cray XMT MTAAP 2010 Eric Goodman (SNL) David Haglin (PNNL) Chad Scherrer (PNNL) Daniel Chavarría-Miranda (PNNL) Jace Mogill (PNNL) John Feo (PNNL) Sandia is a multiprogram laboratory
More informationHuawei FusionCloud Desktop Solution 5.1 Resource Reuse Technical White Paper HUAWEI TECHNOLOGIES CO., LTD. Issue 01.
Huawei FusionCloud Desktop Solution 5.1 Resource Reuse Technical White Paper Issue 01 Date 2014-03-26 HUAWEI TECHNOLOGIES CO., LTD. 2014. All rights reserved. No part of this document may be reproduced
More informationA Classifica*on of Scien*fic Visualiza*on Algorithms for Massive Threading Kenneth Moreland Berk Geveci Kwan- Liu Ma Robert Maynard
A Classifica*on of Scien*fic Visualiza*on Algorithms for Massive Threading Kenneth Moreland Berk Geveci Kwan- Liu Ma Robert Maynard Sandia Na*onal Laboratories Kitware, Inc. University of California at Davis
More informationIntegrating Analysis and Computation with Trios Services
October 31, 2012 Integrating Analysis and Computation with Trios Services Approved for Public Release: SAND2012-9323P Ron A. Oldfield Scalable System Software Sandia National Laboratories Albuquerque,
More informationIn situ visualization is the coupling of visualization
Visualization Viewpoints Editor: Theresa-Marie Rhyne The Tensions of In Situ Visualization Kenneth Moreland Sandia National Laboratories In situ is the coupling of software with a simulation or other data
More informationIntroduction to Visualization on Stampede
Introduction to Visualization on Stampede Aaron Birkland Cornell CAC With contributions from TACC visualization training materials Parallel Computing on Stampede June 11, 2013 From data to Insight Data
More informationThe ParaView Coprocessing Library: A Scalable, General Purpose In Situ Visualization Library
The ParaView Coprocessing Library: A Scalable, General Purpose In Situ Visualization Library Nathan Fabian Sandia National Laboratories Pat Marion Kitware Inc. Berk Geveci Kitware Inc. Ken Moreland Sandia
More informationPorting SLURM to the Cray XT and XE. Neil Stringfellow and Gerrit Renker
Porting SLURM to the Cray XT and XE Neil Stringfellow and Gerrit Renker Background Cray XT/XE basics Cray XT systems are among the largest in the world 9 out of the top 30 machines on the top500 list June
More informationECP Alpine: Algorithms and Infrastructure for In Situ Visualization and Analysis
ECP Alpine: Algorithms and Infrastructure for In Situ Visualization and Analysis Presented By: Matt Larsen LLNL-PRES-731545 This work was performed under the auspices of the U.S. Department of Energy by
More informationIntroduction to Scientific Visualization
Introduction to Scientific Visualization Erik Brisson ebrisson@bu.edu Topics Introduction Visualization techniques Scientific data domains Software packages and workflow Conclusion What is sci-vis? Could
More informationVTK-m: Uniting GPU Acceleration Successes. Robert Maynard Kitware Inc.
VTK-m: Uniting GPU Acceleration Successes Robert Maynard Kitware Inc. VTK-m Project Supercomputer Hardware Advances Everyday More and more parallelism High-Level Parallelism The Free Lunch Is Over (Herb
More informationMassive Remote Batch Visualizer Porting AVS/Express to HECToR
Massive Remote Batch Visualizer Porting AVS/Express to HECToR George Leaver, Martin Turner Research Computing Services, Devonshire House, University of Manchester, Manchester, UK, M13 9PL 2 July 21 Abstract
More informationThe Red Storm System: Architecture, System Update and Performance Analysis
The Red Storm System: Architecture, System Update and Performance Analysis Douglas Doerfler, Jim Tomkins Sandia National Laboratories Center for Computation, Computers, Information and Mathematics LACSI
More information2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or
2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The
More informationOh, Exascale! The effect of emerging architectures on scien1fic discovery. Kenneth Moreland, Sandia Na1onal Laboratories
Photos placed in horizontal posi1on with even amount of white space between photos and header Oh, $#*@! Exascale! The effect of emerging architectures on scien1fic discovery Ultrascale Visualiza1on Workshop,
More informationBatch Scheduling on XT3
Batch Scheduling on XT3 Chad Vizino Pittsburgh Supercomputing Center Overview Simon Scheduler Design Features XT3 Scheduling at PSC Past Present Future Back to the Future! Scheduler Design
More informationHigh Speed Asynchronous Data Transfers on the Cray XT3
High Speed Asynchronous Data Transfers on the Cray XT3 Ciprian Docan, Manish Parashar and Scott Klasky The Applied Software System Laboratory Rutgers, The State University of New Jersey CUG 2007, Seattle,
More informationParaview: A (novice) user perspective
PARAVIEW Copenhagen 2008-1 Paraview: A (novice) user perspective Rony Keppens Centre for Plasma-Astrophysics, K.U.Leuven (Belgium) & FOM-Institute for Plasma Physics Rijnhuizen & Astronomical Institute,
More informationIntroduction to 3D Scientific Visualization. Training in Visualization for PRACE Summer of HPC 2013 Leon Kos, University of Ljubljana, Slovenia
Introduction to 3D Scientific Visualization Training in Visualization for PRACE Summer of HPC 2013 Leon Kos, University of Ljubljana, Slovenia Motto Few correctly put words is worth hundreds of images.
More informationParallel Visualisation on HPCx
Parallel Visualisation on HPCx I. Bethune EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh, EH9 3JZ, UK April 30, 2008 Abstract In order to visualise very large
More informationCompute Node Linux: Overview, Progress to Date & Roadmap
Compute Node Linux: Overview, Progress to Date & Roadmap David Wallace Cray Inc ABSTRACT: : This presentation will provide an overview of Compute Node Linux(CNL) for the CRAY XT machine series. Compute
More informationArcGIS for Server Performance and Scalability Optimizing GIS Services
Esri International User Conference San Diego, California Technical Workshops July 26, 2012 ArcGIS for Server Performance and Scalability Optimizing GIS Services Andrea Rosso (Esri), Craig Williams (Esri),
More informationInsight VisREU Site. Agenda. Introduction to Scientific Visualization Using 6/16/2015. The purpose of visualization is insight, not pictures.
2015 VisREU Site Introduction to Scientific Visualization Using Vetria L. Byrd, Director Advanced Visualization VisREU Site Coordinator REU Site Sponsored by NSF ACI Award 1359223 Introduction to SciVis(High
More informationWhat Can a Small Country Do? The MeteoSwiss Implementation of the COSMO Suite on the Cray XT4
Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz What Can a Small Country Do? The MeteoSwiss Implementation of the COSMO Suite on the Cray XT4 13th ECMWF
More informationAZURE CONTAINER INSTANCES
AZURE CONTAINER INSTANCES -Krunal Trivedi ABSTRACT In this article, I am going to explain what are Azure Container Instances, how you can use them for hosting, when you can use them and what are its features.
More informationParallelizing Windows Operating System Services Job Flows
ABSTRACT SESUG Paper PSA-126-2017 Parallelizing Windows Operating System Services Job Flows David Kratz, D-Wise Technologies Inc. SAS Job flows created by Windows operating system services have a problem:
More informationImprove Web Application Performance with Zend Platform
Improve Web Application Performance with Zend Platform Shahar Evron Zend Sr. PHP Specialist Copyright 2007, Zend Technologies Inc. Agenda Benchmark Setup Comprehensive Performance Multilayered Caching
More informationAsynchronous Termination Detection Module User s Guide
Asynchronous Termination Detection Module User s Guide William McLon III Sandia National Laboratories wcmclen@sandia.gov 1 Introduction Interprocessor communications are performed through point-to-point
More informationmethods of computational science visualization day ii - bottlenecks/parallel-viz santiago v lombeyda center for advanced computing research caltech
methods of computational science visualization day ii - bottlenecks/parallel-viz santiago v lombeyda center for advanced computing research caltech quick review: THE VISUALIZATION PROCESS usual visualization
More informationMastering CMake Fifth Edition
Mastering CMake Fifth Edition Ken Bill Martin & Hoffman With contributions from: Andy Cedilnik, David Cole, Marcus Hanwell, Julien Jomier, Brad King, Alex Neundorf Published by Kitware Inc. Join the CMake
More informationDEVELOPING INTERACTIVE PARALLEL VISUALIZATION FOR COMMODITY-BASED CLUSTERS. November 8, 2002
DEVELOPING INTERACTIVE PARALLEL VISUALIZATION FOR COMMODITY-BASED CLUSTERS S.TOMOV, R.BENNETT, M.MCGUIGAN, A.PESKIN, G.SMITH, AND J.SPILETIC November 8, 2002 Abstract. We present an efficient and inexpensive
More informationA Virtual Comet. HTCondor Week 2017 May Edgar Fajardo On behalf of OSG Software and Technology
A Virtual Comet HTCondor Week 2017 May 3 2017 Edgar Fajardo On behalf of OSG Software and Technology 1 Working in Comet What my friends think I do What Instagram thinks I do What my boss thinks I do 2
More informationCray RS Programming Environment
Cray RS Programming Environment Gail Alverson Cray Inc. Cray Proprietary Red Storm Red Storm is a supercomputer system leveraging over 10,000 AMD Opteron processors connected by an innovative high speed,
More informationBright Cluster Manager
Bright Cluster Manager Using Slurm for Data Aware Scheduling in the Cloud Martijn de Vries CTO About Bright Computing Bright Computing 1. Develops and supports Bright Cluster Manager for HPC systems, server
More informationHigh Throughput WAN Data Transfer with Hadoop-based Storage
High Throughput WAN Data Transfer with Hadoop-based Storage A Amin 2, B Bockelman 4, J Letts 1, T Levshina 3, T Martin 1, H Pi 1, I Sfiligoi 1, M Thomas 2, F Wuerthwein 1 1 University of California, San
More informationScientific Visualization Services at RZG
Scientific Visualization Services at RZG Klaus Reuter, Markus Rampp klaus.reuter@rzg.mpg.de Garching Computing Centre (RZG) 7th GOTiT High Level Course, Garching, 2010 Outline 1 Introduction 2 Details
More informationLubuntu Linux Virtual Machine
Lubuntu Linux 18.04 Virtual Machine About Us Slide / 01 Founded in 2015, Sourcery Institute is a California nonprofit public-benefit corporation engaged in research, education, and advisory services in
More informationMost real programs operate somewhere between task and data parallelism. Our solution also lies in this set.
for Windows Azure and HPC Cluster 1. Introduction In parallel computing systems computations are executed simultaneously, wholly or in part. This approach is based on the partitioning of a big task into
More informationSequence 8.2 Release Notes. Date: 13 th November 2016
Sequence 8.2 Release Notes Date: 13 th November 2016 2016 PNMsoft All Rights Reserved No part of this document may be reproduced in any form by any means without the prior authorization of PNMsoft. PNMsoft
More informationScalable and Distributed Visualization using ParaView
Scalable and Distributed Visualization using ParaView Eric A. Wernert, Ph.D. Senior Manager & Scientist, Advanced Visualization Lab Pervasive Technology Institute, Indiana University Big Data for Science
More informationVisIt Overview. VACET: Chief SW Engineer ASC: V&V Shape Char. Lead. Hank Childs. Supercomputing 2006 Tampa, Florida November 13, 2006
VisIt Overview Hank Childs VACET: Chief SW Engineer ASC: V&V Shape Char. Lead Supercomputing 2006 Tampa, Florida November 13, 2006 27B element Rayleigh-Taylor Instability (MIRANDA, BG/L) This is UCRL-PRES-226373
More informationThe Use of Cloud Computing Resources in an HPC Environment
The Use of Cloud Computing Resources in an HPC Environment Bill, Labate, UCLA Office of Information Technology Prakashan Korambath, UCLA Institute for Digital Research & Education Cloud computing becomes
More informationPhilip C. Roth. Computer Science and Mathematics Division Oak Ridge National Laboratory
Philip C. Roth Computer Science and Mathematics Division Oak Ridge National Laboratory A Tree-Based Overlay Network (TBON) like MRNet provides scalable infrastructure for tools and applications MRNet's
More informationInteractively Visualizing Science at Scale
Interactively Visualizing Science at Scale Kelly Gaither Director of Visualization/Senior Research Scientist Texas Advanced Computing Center November 13, 2012 Issues and Concerns Maximizing Scientific
More informationZEST Snapshot Service. A Highly Parallel Production File System by the PSC Advanced Systems Group Pittsburgh Supercomputing Center 1
ZEST Snapshot Service A Highly Parallel Production File System by the PSC Advanced Systems Group Pittsburgh Supercomputing Center 1 Design Motivation To optimize science utilization of the machine Maximize
More informationMoab Workload Manager on Cray XT3
Moab Workload Manager on Cray XT3 presented by Don Maxwell (ORNL) Michael Jackson (Cluster Resources, Inc.) MOAB Workload Manager on Cray XT3 Why MOAB? Requirements Features Support/Futures 2 Why Moab?
More informationA Reference Architecture for Payload Reusable Software (RAPRS)
SAND2011-7588 C A Reference Architecture for Payload Reusable Software (RAPRS) 2011 Workshop on Spacecraft Flight Software Richard D. Hunt Sandia National Laboratories P.O. Box 5800 M/S 0513 Albuquerque,
More informationCompute Node Linux (CNL) The Evolution of a Compute OS
Compute Node Linux (CNL) The Evolution of a Compute OS Overview CNL The original scheme plan, goals, requirements Status of CNL Plans Features and directions Futures May 08 Cray Inc. Proprietary Slide
More informationIntroduction to MapReduce
Introduction to MapReduce April 19, 2012 Jinoh Kim, Ph.D. Computer Science Department Lock Haven University of Pennsylvania Research Areas Datacenter Energy Management Exa-scale Computing Network Performance
More informationNAMD Serial and Parallel Performance
NAMD Serial and Parallel Performance Jim Phillips Theoretical Biophysics Group Serial performance basics Main factors affecting serial performance: Molecular system size and composition. Cutoff distance
More informationAdding a System Call to Plan 9
Adding a System Call to Plan 9 John Floren (john@csplan9.rit.edu) Sandia National Laboratories Livermore, CA 94551 DOE/NNSA Funding Statement Sandia is a multiprogram laboratory operated by Sandia Corporation,
More informationMicroservices Beyond the Hype. SATURN San Diego May 3, 2016 Paulo Merson
Microservices Beyond the Hype SATURN San Diego May 3, 2016 Paulo Merson Our goal Try to define microservice Discuss what you gain and what you lose with microservices 2 Defining Microservice Unfortunately
More informationVisualization with ParaView
Visualization with Before we begin Make sure you have 3.10.1 installed so you can follow along in the lab section http://paraview.org/paraview/resources/software.html http://www.paraview.org/ Background
More informationEnabling In Situ Viz and Data Analysis with Provenance in libmesh
Enabling In Situ Viz and Data Analysis with Provenance in libmesh Vítor Silva Jose J. Camata Marta Mattoso Alvaro L. G. A. Coutinho (Federal university Of Rio de Janeiro/Brazil) Patrick Valduriez (INRIA/France)
More informationEnabling Contiguous Node Scheduling on the Cray XT3
Enabling Contiguous Node Scheduling on the Cray XT3 Chad Vizino Pittsburgh Supercomputing Center Cray User Group Helsinki, Finland May 2008 With acknowledgements to Deborah Weisser, Jared Yanovich, Derek
More informationIDERA HELPS PPG INDUSTRIES REDUCE SQL SERVER BACKUP STORAGE COSTS BY OVER 70%
IDERA HELPS PPG INDUSTRIES REDUCE SQL SERVER BACKUP STORAGE COSTS BY OVER 70% PPG LEVERAGES IDERA S ON 60 MICROSOFT SQL SERVERS ACROSS THE US AND CANADA PROCESSING MORE THAN 3,400 BACKUPS PER DAY PPG Industries,
More informationThis course is designed for anyone who needs to learn how to write programs in Python.
Python Programming COURSE OVERVIEW: This course introduces the student to the Python language. Upon completion of the course, the student will be able to write non-trivial Python programs dealing with
More informationRemote & Collaborative Visualization. Texas Advanced Computing Center
Remote & Collaborative Visualization Texas Advanced Computing Center TACC Remote Visualization Systems Longhorn NSF XD Dell Visualization Cluster 256 nodes, each 8 cores, 48 GB (or 144 GB) memory, 2 NVIDIA
More informationSLURM Operation on Cray XT and XE
SLURM Operation on Cray XT and XE Morris Jette jette@schedmd.com Contributors and Collaborators This work was supported by the Oak Ridge National Laboratory Extreme Scale Systems Center. Swiss National
More informationSecurity Metrics. Mark Torgerson Sandia National Laboratories 6/20/2007. Entry I-108
Security Metrics Mark Torgerson Sandia National Laboratories 6/20/2007 Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of
More informationWeb Services for Visualization
Web Services for Visualization Gordon Erlebacher (Florida State University) Collaborators: S. Pallickara, G. Fox (Indiana U.) Dave Yuen (U. Minnesota) State of affairs Size of datasets is growing exponentially
More informationLecture 9: MIMD Architectures
Lecture 9: MIMD Architectures Introduction and classification Symmetric multiprocessors NUMA architecture Clusters Zebo Peng, IDA, LiTH 1 Introduction MIMD: a set of general purpose processors is connected
More informationNaaS Network-as-a-Service in the Cloud
NaaS Network-as-a-Service in the Cloud joint work with Matteo Migliavacca, Peter Pietzuch, and Alexander L. Wolf costa@imperial.ac.uk Motivation Mismatch between app. abstractions & network How the programmers
More informationChapter 5. The MapReduce Programming Model and Implementation
Chapter 5. The MapReduce Programming Model and Implementation - Traditional computing: data-to-computing (send data to computing) * Data stored in separate repository * Data brought into system for computing
More informationIQ for DNA. Interactive Query for Dynamic Network Analytics. Haoyu Song. HUAWEI TECHNOLOGIES Co., Ltd.
IQ for DNA Interactive Query for Dynamic Network Analytics Haoyu Song www.huawei.com Motivation Service Provider s pain point Lack of real-time and full visibility of networks, so the network monitoring
More information1 Connectionless Routing
UCSD DEPARTMENT OF COMPUTER SCIENCE CS123a Computer Networking, IP Addressing and Neighbor Routing In these we quickly give an overview of IP addressing and Neighbor Routing. Routing consists of: IP addressing
More informationGRID Application Portal
Martin Matusiak 1 Jonas Lindemann 2 1 The NTNU High Performance Computing Project Norwegian University of Science and Technology 2 Lunarc, Center for Scientific and Technical Computing Lund University
More informationIDL DISCOVER WHAT S IN YOUR DATA
IDL DISCOVER WHAT S IN YOUR DATA IDL Discover What s In Your Data. A key foundation of scientific discovery is complex numerical data. If making discoveries is a fundamental part of your work, you need
More informationChris Sewell Li-Ta Lo James Ahrens Los Alamos National Laboratory
Portability and Performance for Visualization and Analysis Operators Using the Data-Parallel PISTON Framework Chris Sewell Li-Ta Lo James Ahrens Los Alamos National Laboratory Outline! Motivation Portability
More informationOptimizing Parallel Access to the BaBar Database System Using CORBA Servers
SLAC-PUB-9176 September 2001 Optimizing Parallel Access to the BaBar Database System Using CORBA Servers Jacek Becla 1, Igor Gaponenko 2 1 Stanford Linear Accelerator Center Stanford University, Stanford,
More informationHigh Performance Data Analytics for Numerical Simulations. Bruno Raffin DataMove
High Performance Data Analytics for Numerical Simulations Bruno Raffin DataMove bruno.raffin@inria.fr April 2016 About this Talk HPC for analyzing the results of large scale parallel numerical simulations
More informationTeko: A Package for Multiphysics Preconditioners
SAND 2009-7242P Teko: A Package for Multiphysics Preconditioners Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energyʼs
More informationPost-processing with Paraview. R. Ponzini, CINECA -SCAI
Post-processing with Paraview R. Ponzini, CINECA -SCAI Post-processing with Paraview: Overall Program Post-processing with Paraview I (ParaView GUI and Filters) Post-processing with Paraview II (ParaView
More informationUnlimited Scalability in the Cloud A Case Study of Migration to Amazon DynamoDB
Unlimited Scalability in the Cloud A Case Study of Migration to Amazon DynamoDB Steve Saporta CTO, SpinCar Mar 19, 2016 SpinCar When a web-based business grows... More customers = more transactions More
More informationTrafficDB: HERE s High Performance Shared-Memory Data Store Ricardo Fernandes, Piotr Zaczkowski, Bernd Göttler, Conor Ettinoffe, and Anis Moussa
TrafficDB: HERE s High Performance Shared-Memory Data Store Ricardo Fernandes, Piotr Zaczkowski, Bernd Göttler, Conor Ettinoffe, and Anis Moussa EPL646: Advanced Topics in Databases Christos Hadjistyllis
More informationWhat are Clusters? Why Clusters? - a Short History
What are Clusters? Our definition : A parallel machine built of commodity components and running commodity software Cluster consists of nodes with one or more processors (CPUs), memory that is shared by
More informationPODShell: Simplifying HPC in the Cloud Workflow
PODShell: Simplifying HPC in the Cloud Workflow June 2011 Penguin provides Linux HPC Solutions Linux Systems Servers Workstations Cluster Management Software HPC as a Service - Penguin on Demand Professional
More informationHigh Availability Distributed (Micro-)services. Clemens Vasters Microsoft
High Availability Distributed (Micro-)services Clemens Vasters Microsoft Azure @clemensv ice Microsoft Azure services I work(-ed) on. Notification Hubs Service Bus Event Hubs Event Grid IoT Hub Relay Mobile
More informationChristopher Sewell Katrin Heitmann Li-ta Lo Salman Habib James Ahrens
LA-UR- 14-25437 Approved for public release; distribution is unlimited. Title: Portable Parallel Halo and Center Finders for HACC Author(s): Christopher Sewell Katrin Heitmann Li-ta Lo Salman Habib James
More informationPerformance of a Direct Numerical Simulation Solver forf Combustion on the Cray XT3/4
Performance of a Direct Numerical Simulation Solver forf Combustion on the Cray XT3/4 Ramanan Sankaran and Mark R. Fahey National Center for Computational Sciences Oak Ridge National Laboratory Jacqueline
More informationArcGIS for Server: Administration and Security. Amr Wahba
ArcGIS for Server: Administration and Security Amr Wahba awahba@esri.com Agenda ArcGIS Server architecture Distributing and scaling components Implementing security Monitoring server logs Automating server
More informationPROCE55 Mobile: Web API App. Web API. https://www.rijksmuseum.nl/api/...
PROCE55 Mobile: Web API App PROCE55 Mobile with Test Web API App Web API App Example This example shows how to access a typical Web API using your mobile phone via Internet. The returned data is in JSON
More informationRUNNING MOLECULAR DYNAMICS SIMULATIONS WITH CHARMM: A BRIEF TUTORIAL
RUNNING MOLECULAR DYNAMICS SIMULATIONS WITH CHARMM: A BRIEF TUTORIAL While you can probably write a reasonable program that carries out molecular dynamics (MD) simulations, it s sometimes more efficient
More informationCloudBATCH: A Batch Job Queuing System on Clouds with Hadoop and HBase. Chen Zhang Hans De Sterck University of Waterloo
CloudBATCH: A Batch Job Queuing System on Clouds with Hadoop and HBase Chen Zhang Hans De Sterck University of Waterloo Outline Introduction Motivation Related Work System Design Future Work Introduction
More informationIn-Situ Data Analysis and Visualization: ParaView, Calalyst and VTK-m
In-Situ Data Analysis and Visualization: ParaView, Calalyst and VTK-m GTC, San Jose, CA March, 2015 Robert Maynard Marcus D. Hanwell 1 Agenda Introduction to ParaView Catalyst Run example Catalyst Script
More informationVulkan: Scaling to Multiple Threads. Kevin sun Lead Developer Support Engineer, APAC PowerVR Graphics
Vulkan: Scaling to Multiple Threads Kevin sun Lead Developer Support Engineer, APAC PowerVR Graphics www.imgtec.com Introduction Who am I? Kevin Sun Working at Imagination Technologies Take responsibility
More informationVisualization Systems. Ronald Peikert SciVis Visualization Systems 11-1
Visualization Systems Ronald Peikert SciVis 2008 - Visualization Systems 11-1 Modular visualization environments Many popular visualization software are designed as socalled modular visualization environments
More informationAdventures in Load Balancing at Scale: Successes, Fizzles, and Next Steps
Adventures in Load Balancing at Scale: Successes, Fizzles, and Next Steps Rusty Lusk Mathematics and Computer Science Division Argonne National Laboratory Outline Introduction Two abstract programming
More informationVARIABILITY IN OPERATING SYSTEMS
VARIABILITY IN OPERATING SYSTEMS Brian Kocoloski Assistant Professor in CSE Dept. October 8, 2018 1 CLOUD COMPUTING Current estimate is that 94% of all computation will be performed in the cloud by 2021
More informationIS TOPOLOGY IMPORTANT AGAIN? Effects of Contention on Message Latencies in Large Supercomputers
IS TOPOLOGY IMPORTANT AGAIN? Effects of Contention on Message Latencies in Large Supercomputers Abhinav S Bhatele and Laxmikant V Kale ACM Research Competition, SC 08 Outline Why should we consider topology
More information8. Solving Stochastic Programs
8. Solving Stochastic Programs Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S.
More informationEarly X1 Experiences at Boeing. Jim Glidewell Information Technology Services Boeing Shared Services Group
Early X1 Experiences at Boeing Jim Glidewell Information Technology Services Boeing Shared Services Group Early X1 Experiences at Boeing HPC computing environment X1 configuration Hardware and OS Applications
More informationVisual Design Flows for Faster Debug and Time to Market FlowTracer White Paper
Visual Design Flows for Faster Debug and Time to Market FlowTracer White Paper 2560 Mission College Blvd., Suite 130 Santa Clara, CA 95054 (408) 492-0940 Introduction As System-on-Chip (SoC) designs have
More informationHTCondor on Titan. Wisconsin IceCube Particle Astrophysics Center. Vladimir Brik. HTCondor Week May 2018
HTCondor on Titan Wisconsin IceCube Particle Astrophysics Center Vladimir Brik HTCondor Week May 2018 Overview of Titan Cray XK7 Supercomputer at Oak Ridge Leadership Computing Facility Ranked #5 by TOP500
More information